macro_australia_retail_rate_monthly

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds a source URL (https://data.eastmoney.com/cjsj/foreign_5_0.html) and the return type as a pandas.DataFrame containing '零售销售月率', which provides some context about the data origin and output. However, it does not disclose other behavioral traits such as data frequency, time range, or any network dependency, so the added value is limited to basic source/return information.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short and contains no fluff, but it is formatted like a docstring label rather than an explanatory entry: a title line, a URL, and a return annotation. It is under-specified for a functional description, yet it does present the minimal metadata efficiently. The structure is acceptable but not well-balanced; it front-loads a title that repeats the tool name instead of a clear purpose statement.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the tool's simplicity (0 params, read-only, no output schema), the description is not fully self-contained. It fails to state in a clear sentence what the tool does, leaving the agent to infer from the name. It also lacks any differentiation from the large set of sibling tools covering other Australian economic indicators, and does not describe the data structure beyond a vague return label. The annotations cover safety, but the description does not provide sufficient domain context for reliable selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and the input schema is empty, so there is nothing for the description to explain regarding parameters. Per the rubric, a 0-parameter tool receives a baseline of 4 since no parameter documentation is needed. The description correctly omits any parameter-related content.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description is essentially a Chinese translation of the tool name ('东方财富-经济数据-澳大利亚-零售销售月率'), which repeats the semantics already in 'macro_australia_retail_rate_monthly'. It lacks an explicit verb or action statement, so it reads as a heading rather than a functional description. It also does not differentiate this tool from siblings like macro_australia_cpi_yearly or macro_australia_trade, all of which are similarly named.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus the many other Australian macroeconomic tools. The description provides only a URL and a return annotation, with no mention of alternatives, prerequisites, or specific use cases. The agent must infer the appropriate context solely from the tool name, which the description does not support or clarify.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.